Constrained Model Predictive Control in Nine-Phase Induction Motor Drives
The advent of powerful digital signal processors has recently permitted the real-time implementation of model predictive control (MPC) in high-performance electric drives. Nevertheless, the use of MPC together with multiphase systems is increasingly challenging as the number of phases gets higher. O...
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| Vydáno v: | IEEE transactions on energy conversion Ročník 34; číslo 4; s. 1881 - 1889 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0885-8969, 1558-0059 |
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| Abstract | The advent of powerful digital signal processors has recently permitted the real-time implementation of model predictive control (MPC) in high-performance electric drives. Nevertheless, the use of MPC together with multiphase systems is increasingly challenging as the number of phases gets higher. On the positive side, the redundancy provided by the extra phases also opens the possibility to further optimize the control action. This paper describes the implementation of MPC for nine-phase drives using a three-step approach with an initial discarding of the switching states, a dynamic selection of the voltage vectors using hard constraints, and an improved performance including soft constraints. Experimental results confirm the ability of the proposed MPC to highly reduce the computational burden and switching frequency, while maintaining satisfactory steady state and dynamic performance. |
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| AbstractList | The advent of powerful digital signal processors has recently permitted the real-time implementation of model predictive control (MPC) in high-performance electric drives. Nevertheless, the use of MPC together with multiphase systems is increasingly challenging as the number of phases gets higher. On the positive side, the redundancy provided by the extra phases also opens the possibility to further optimize the control action. This paper describes the implementation of MPC for nine-phase drives using a three-step approach with an initial discarding of the switching states, a dynamic selection of the voltage vectors using hard constraints, and an improved performance including soft constraints. Experimental results confirm the ability of the proposed MPC to highly reduce the computational burden and switching frequency, while maintaining satisfactory steady state and dynamic performance. |
| Author | Duran, Mario J. Zoric, Ivan Levi, Emil Gonzalez-Prieto, Ignacio |
| Author_xml | – sequence: 1 givenname: Ignacio orcidid: 0000-0001-5028-9402 surname: Gonzalez-Prieto fullname: Gonzalez-Prieto, Ignacio email: ignaciogp87@gmail.com organization: Department of Electrical Engineering, University of Malaga, Malaga, Spain – sequence: 2 givenname: Ivan orcidid: 0000-0002-2737-8842 surname: Zoric fullname: Zoric, Ivan email: ivanzoric497@gmail.com organization: Dyson, Malmesbury, U.K – sequence: 3 givenname: Mario J. orcidid: 0000-0002-4912-7091 surname: Duran fullname: Duran, Mario J. email: mjduran@uma.es organization: Department of Electrical Engineering, University of Malaga, Malaga, Spain – sequence: 4 givenname: Emil orcidid: 0000-0001-6504-1522 surname: Levi fullname: Levi, Emil email: e.levi@ljmu.ac.uk organization: Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, U.K |
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| StartPage | 1881 |
| SubjectTerms | Computational efficiency Constraints Digital signal processors Electric drives Induction motor drives Induction motors model predictive control Multiphase drives Predictive control Real-time systems Redundancy Signal processing Switches Switching Switching frequency |
| Title | Constrained Model Predictive Control in Nine-Phase Induction Motor Drives |
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